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COVID-19 emergency decision-making using q-rung linear diophantine fuzzy set, differential evolutionary and evidential reasoning techniques

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Abstract

In this paper, a robust and consistent COVID-19 emergency decision-making approach is proposed based on q-rung linear diophantine fuzzy set (q-RLDFS), differential evolutionary (DE) optimization principles, and evidential reasoning (ER) methodology. The proposed approach uses q-RLDFS in order to represent the evaluating values of the alternatives corresponding to the attributes. DE optimization is used to obtain the optimal weights of the attributes, and ER methodology is used to compute the aggregated q-rung linear diophantine fuzzy values (q-RLDFVs) of each alternative. Then the score values of alternatives are computed based on the aggregated q-RLDFVs. An alternative with the maximum score value is selected as a better one. The applicability of the proposed approach has been illustrated in COVID-19 emergency decision-making system and sustainable energy planning management. Moreover, we have validated the proposed approach with a numerical example. Finally, a comparative study is provided with the existing models, where the proposed approach is found to be robust to perform better and consistent in uncertain environments.

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Authors and Affiliations

  1. Department of Computer science and Engineering, National Institute of Technology, Warangal, 506004, India

    G. Punnam Chander & Sujit Das

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  1. G. Punnam Chander
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  2. Sujit Das
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Correspondence to G. Punnam Chander or Sujit Das.

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Conflict of interest The authors declare no conflict of interest.

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Chander, G.P., Das, S. COVID-19 emergency decision-making using q-rung linear diophantine fuzzy set, differential evolutionary and evidential reasoning techniques. Appl. Math. J. Chin. Univ. 40, 182–206 (2025). https://doi.org/10.1007/s11766-025-4550-0

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  • DOI: https://doi.org/10.1007/s11766-025-4550-0

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